Predictive Linear-Gaussian Models of Dynamical Systems with Vector-Valued Actions and Observations
نویسندگان
چکیده
Predictive state representations use probabilities of future events as the state of a partially observable system, as opposed to most classical models, which use probabilistic statements about latent variables as state. We present a new version of the predictive linear-Gaussian model (PLG), a predictive state representation that models discrete-time dynamical systems with real-vector-valued actions and observations. This extends earlier work on PLGs in which the dynamical systems were limited to scalar observations. We show that the new PLG subsumes linear dynamical systems (LDSs, sometimes called Kalman filter models) of equal dimension. Finally, we introduce an algorithm to estimate PLG parameters from data and show that our algorithm is a consistent estimation procedure.
منابع مشابه
Predictive Linear-Gaussian Models of Stochastic Dynamical Systems with Vector-Valued Actions and Observations
Predictive state representations use probabilities of future events as the state of a partially observable system, as opposed to most classical models, which use probabilistic statements about latent variables as state. We present a new version of the predictive linear-Gaussian model (PLG), a predictive state representation that models discrete-time dynamical systems with real-vector-valued act...
متن کاملPredictive Linear-Gaussian Models of Stochastic Dynamical Systems
Models of dynamical systems based on predictive state representations (PSRs) are defined strictly in terms of observable quantities, in contrast with traditional models (such as Hidden Markov Models) that use latent variables or statespace representations. In addition, PSRs have an effectively infinite memory, allowing them to model some systems that finite memory-based models cannot. Thus far,...
متن کاملLatent Variable and Predictive Models of Dynamical Systems
The modeling of discrete-time dynamical systems under uncertainty is an important endeavor, with applications in a wide range of fields. We propose to investigate new models and algorithms for inference, structure and parameter learning that extend our capabilities of modeling such systems, with a focus on models for real-valued sequential data. Our work is grounded in existing generative model...
متن کاملMixtures of Predictive Linear Gaussian Models for Nonlinear, Stochastic Dynamical Systems
The Predictive Linear Gaussian model (or PLG) improves upon traditional linear dynamical system models by using a predictive representation of state, which makes consistent parameter estimation possible without any loss of modeling power and while using fewer parameters. This work extends the PLG to model nonlinear dynamical systems through the use of a kernelized, nonlinear mixture technique. ...
متن کاملLearning Latent Variable and Predictive Models of Dynamical Systems
A variety of learning problems in robotics, computer vision and other areas of artificial intelligence can be construed as problems of learning statistical models for dynamical systems from sequential observations. Good dynamical system models allow us to represent and predict observations in these systems, which in turn enables applications such as classification, planning, control, simulation...
متن کامل